RGB Depth alignment

This example shows usage of RGB depth alignment. Since OAK-D has a color and a pair of stereo cameras, you can align depth map to the color frame on top of that to get RGB depth.

Demo

https://user-images.githubusercontent.com/18037362/151351377-a5752fbe-3b8b-4985-b8d1-d5f8a7d5a868.png

Setup

Please run the install script to download all required dependencies. Please note that this script must be ran from git context, so you have to download the depthai-python repository first and then run the script

git clone https://github.com/luxonis/depthai-python.git
cd depthai-python/examples
python3 install_requirements.py

For additional information, please follow installation guide

Source code

Python

C++

Also available on GitHub

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#!/usr/bin/env python3

import cv2
import numpy as np
import depthai as dai

# Weights to use when blending depth/rgb image (should equal 1.0)
rgbWeight = 0.4
depthWeight = 0.6


def updateBlendWeights(percent_rgb):
    """
    Update the rgb and depth weights used to blend depth/rgb image

    @param[in] percent_rgb The rgb weight expressed as a percentage (0..100)
    """
    global depthWeight
    global rgbWeight
    rgbWeight = float(percent_rgb)/100.0
    depthWeight = 1.0 - rgbWeight


# Optional. If set (True), the ColorCamera is downscaled from 1080p to 720p.
# Otherwise (False), the aligned depth is automatically upscaled to 1080p
downscaleColor = True
fps = 30
# The disparity is computed at this resolution, then upscaled to RGB resolution
monoResolution = dai.MonoCameraProperties.SensorResolution.THE_720_P

# Create pipeline
pipeline = dai.Pipeline()
queueNames = []

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
left = pipeline.create(dai.node.MonoCamera)
right = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)

rgbOut = pipeline.create(dai.node.XLinkOut)
disparityOut = pipeline.create(dai.node.XLinkOut)

rgbOut.setStreamName("rgb")
queueNames.append("rgb")
disparityOut.setStreamName("disp")
queueNames.append("disp")

#Properties
camRgb.setBoardSocket(dai.CameraBoardSocket.RGB)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setFps(fps)
if downscaleColor: camRgb.setIspScale(2, 3)
# For now, RGB needs fixed focus to properly align with depth.
# This value was used during calibration
camRgb.initialControl.setManualFocus(130)

left.setResolution(monoResolution)
left.setBoardSocket(dai.CameraBoardSocket.LEFT)
left.setFps(fps)
right.setResolution(monoResolution)
right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
right.setFps(fps)

stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# LR-check is required for depth alignment
stereo.setLeftRightCheck(True)
stereo.setDepthAlign(dai.CameraBoardSocket.RGB)

# Linking
camRgb.isp.link(rgbOut.input)
left.out.link(stereo.left)
right.out.link(stereo.right)
stereo.disparity.link(disparityOut.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    frameRgb = None
    frameDisp = None

    # Configure windows; trackbar adjusts blending ratio of rgb/depth
    rgbWindowName = "rgb"
    depthWindowName = "depth"
    blendedWindowName = "rgb-depth"
    cv2.namedWindow(rgbWindowName)
    cv2.namedWindow(depthWindowName)
    cv2.namedWindow(blendedWindowName)
    cv2.createTrackbar('RGB Weight %', blendedWindowName, int(rgbWeight*100), 100, updateBlendWeights)

    while True:
        latestPacket = {}
        latestPacket["rgb"] = None
        latestPacket["disp"] = None

        queueEvents = device.getQueueEvents(("rgb", "disp"))
        for queueName in queueEvents:
            packets = device.getOutputQueue(queueName).tryGetAll()
            if len(packets) > 0:
                latestPacket[queueName] = packets[-1]

        if latestPacket["rgb"] is not None:
            frameRgb = latestPacket["rgb"].getCvFrame()
            cv2.imshow(rgbWindowName, frameRgb)

        if latestPacket["disp"] is not None:
            frameDisp = latestPacket["disp"].getFrame()
            maxDisparity = stereo.initialConfig.getMaxDisparity()
            # Optional, extend range 0..95 -> 0..255, for a better visualisation
            if 1: frameDisp = (frameDisp * 255. / maxDisparity).astype(np.uint8)
            # Optional, apply false colorization
            if 1: frameDisp = cv2.applyColorMap(frameDisp, cv2.COLORMAP_HOT)
            frameDisp = np.ascontiguousarray(frameDisp)
            cv2.imshow(depthWindowName, frameDisp)

        # Blend when both received
        if frameRgb is not None and frameDisp is not None:
            # Need to have both frames in BGR format before blending
            if len(frameDisp.shape) < 3:
                frameDisp = cv2.cvtColor(frameDisp, cv2.COLOR_GRAY2BGR)
            blended = cv2.addWeighted(frameRgb, rgbWeight, frameDisp, depthWeight, 0)
            cv2.imshow(blendedWindowName, blended)
            frameRgb = None
            frameDisp = None

        if cv2.waitKey(1) == ord('q'):
            break

Also available on GitHub

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#include <cstdio>
#include <iostream>

#include "utility.hpp"

// Includes common necessary includes for development using depthai library
#include "depthai/depthai.hpp"

// Optional. If set (true), the ColorCamera is downscaled from 1080p to 720p.
// Otherwise (false), the aligned depth is automatically upscaled to 1080p
static std::atomic<bool> downscaleColor{true};
static constexpr int fps = 30;
// The disparity is computed at this resolution, then upscaled to RGB resolution
static constexpr auto monoRes = dai::MonoCameraProperties::SensorResolution::THE_720_P;

static float rgbWeight = 0.4f;
static float depthWeight = 0.6f;

static void updateBlendWeights(int percentRgb, void* ctx) {
    rgbWeight = float(percentRgb) / 100.f;
    depthWeight = 1.f - rgbWeight;
}

int main() {
    using namespace std;

    // Create pipeline
    dai::Pipeline pipeline;
    std::vector<std::string> queueNames;

    // Define sources and outputs
    auto camRgb = pipeline.create<dai::node::ColorCamera>();
    auto left = pipeline.create<dai::node::MonoCamera>();
    auto right = pipeline.create<dai::node::MonoCamera>();
    auto stereo = pipeline.create<dai::node::StereoDepth>();

    auto rgbOut = pipeline.create<dai::node::XLinkOut>();
    auto depthOut = pipeline.create<dai::node::XLinkOut>();

    rgbOut->setStreamName("rgb");
    queueNames.push_back("rgb");
    depthOut->setStreamName("depth");
    queueNames.push_back("depth");

    // Properties
    camRgb->setBoardSocket(dai::CameraBoardSocket::RGB);
    camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_1080_P);
    camRgb->setFps(fps);
    if(downscaleColor) camRgb->setIspScale(2, 3);
    // For now, RGB needs fixed focus to properly align with depth.
    // This value was used during calibration
    camRgb->initialControl.setManualFocus(135);

    left->setResolution(monoRes);
    left->setBoardSocket(dai::CameraBoardSocket::LEFT);
    left->setFps(fps);
    right->setResolution(monoRes);
    right->setBoardSocket(dai::CameraBoardSocket::RIGHT);
    right->setFps(fps);

    stereo->setDefaultProfilePreset(dai::node::StereoDepth::PresetMode::HIGH_DENSITY);
    // LR-check is required for depth alignment
    stereo->setLeftRightCheck(true);
    stereo->setDepthAlign(dai::CameraBoardSocket::RGB);

    // Linking
    camRgb->isp.link(rgbOut->input);
    left->out.link(stereo->left);
    right->out.link(stereo->right);
    stereo->disparity.link(depthOut->input);

    // Connect to device and start pipeline
    dai::Device device(pipeline);

    // Sets queues size and behavior
    for(const auto& name : queueNames) {
        device.getOutputQueue(name, 4, false);
    }

    std::unordered_map<std::string, cv::Mat> frame;

    auto rgbWindowName = "rgb";
    auto depthWindowName = "depth";
    auto blendedWindowName = "rgb-depth";
    cv::namedWindow(rgbWindowName);
    cv::namedWindow(depthWindowName);
    cv::namedWindow(blendedWindowName);
    int defaultValue = (int)(rgbWeight * 100);
    cv::createTrackbar("RGB Weight %", blendedWindowName, &defaultValue, 100, updateBlendWeights);

    while(true) {
        std::unordered_map<std::string, std::shared_ptr<dai::ImgFrame>> latestPacket;

        auto queueEvents = device.getQueueEvents(queueNames);
        for(const auto& name : queueEvents) {
            auto packets = device.getOutputQueue(name)->tryGetAll<dai::ImgFrame>();
            auto count = packets.size();
            if(count > 0) {
                latestPacket[name] = packets[count - 1];
            }
        }

        for(const auto& name : queueNames) {
            if(latestPacket.find(name) != latestPacket.end()) {
                if(name == depthWindowName) {
                    frame[name] = latestPacket[name]->getFrame();
                    auto maxDisparity = stereo->initialConfig.getMaxDisparity();
                    // Optional, extend range 0..95 -> 0..255, for a better visualisation
                    if(1) frame[name].convertTo(frame[name], CV_8UC1, 255. / maxDisparity);
                    // Optional, apply false colorization
                    if(1) cv::applyColorMap(frame[name], frame[name], cv::COLORMAP_HOT);
                } else {
                    frame[name] = latestPacket[name]->getCvFrame();
                }

                cv::imshow(name, frame[name]);
            }
        }

        // Blend when both received
        if(frame.find(rgbWindowName) != frame.end() && frame.find(depthWindowName) != frame.end()) {
            // Need to have both frames in BGR format before blending
            if(frame[depthWindowName].channels() < 3) {
                cv::cvtColor(frame[depthWindowName], frame[depthWindowName], cv::COLOR_GRAY2BGR);
            }
            cv::Mat blended;
            cv::addWeighted(frame[rgbWindowName], rgbWeight, frame[depthWindowName], depthWeight, 0, blended);
            cv::imshow(blendedWindowName, blended);
            frame.clear();
        }

        int key = cv::waitKey(1);
        if(key == 'q' || key == 'Q') {
            return 0;
        }
    }
    return 0;
}

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